Data Science with Python Online Course by Karishma Nikhil Kochar

This Data Science with Python online course is a comprehensive program designed for students, graduates, and working professionals who want to build strong foundations and practical skills in data science, analytics, and machine learning. Conducted by Karishma Nikhil Kochar, the course covers the complete data science workflow—from understanding data and preparing it for analysis to building machine learning models and working on real-world projects. The curriculum is structured to help learners progress step by step, even if they are new to data science, while gradually introducing advanced analytical and modeling concepts.

What Students Will Learn

Introduction to Data Science

• What data science is and how it is used

• Data science lifecycle and workflows

• Types of data: structured and unstructured

• Real-world applications and use cases

• Career roles: Data Analyst, Data Scientist, ML Engineer

Python for Data Science

• Python basics: syntax, variables, and data types

• Control flow and functions

• Object-oriented programming basics

• File handling and exception handling

• NumPy for numerical computing

• Pandas for data manipulation and analysis

• Data visualization using Matplotlib and Seaborn

Mathematics for Data Science

• Statistics fundamentals

• Mean, median, and mode

• Variance and standard deviation

• Probability and data distributions

• Hypothesis testing concepts

• Linear algebra basics

• Correlation and covariance

Data Cleaning & Preprocessing

• Handling missing values

• Outlier detection techniques

• Data transformation methods

• Feature scaling: normalization and standardization

• Encoding categorical variables

• Feature engineering concepts

Exploratory Data Analysis (EDA)

• Data visualization techniques

• Univariate and multivariate analysis

• Identifying trends and patterns

• Extracting insights and reporting findings

Machine Learning

Supervised Learning

• Linear regression

• Logistic regression

• K-Nearest Neighbors (KNN)

• Decision trees

• Random forest

• Support vector machines

Unsupervised Learning

• K-means clustering

• Hierarchical clustering

• DBSCAN

• Principal Component Analysis (PCA)

Deep Learning Basics

• Neural network fundamentals

• Perceptron and artificial neural networks (ANN)

• Introduction to TensorFlow and Keras

• Conceptual understanding of CNN and RNN

SQL for Data Science

• SQL fundamentals

• Joins and subqueries

• Aggregations and window functions

• Using SQL for data analysis

Real-World Projects

• Hands-on projects to apply concepts

• End-to-end data analysis and modeling workflow

• Practical exposure to real datasets

Teaching Method

• Mode: Online live classes

• Structured, step-by-step curriculum

• Concept explanation with practical coding demonstrations

• Hands-on exercises and project-based learning

• Regular doubt-clearing sessions

• Focus on real-world data science workflows

Why This Tutor

Karishma Nikhil Kochar follows a concept-first and application-oriented teaching approach, ensuring learners understand both the theory and practical implementation of data science techniques. The course structure supports gradual skill development, making complex topics approachable for learners from diverse academic backgrounds.

Benefits / Outcomes

• Strong understanding of the complete data science lifecycle

• Practical skills in Python, data analysis, and visualization

• Ability to clean, analyze, and interpret real-world datasets

• Foundational knowledge of machine learning and deep learning

• Improved confidence to work on data-driven projects and roles